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pfsa-id-med-indobert-lem – AI Model by damand2061 | AlphaNeural AI
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damand2061
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pfsa-id-med-indobert-lem
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transformers
tf
bert
token-classification
generated_from_keras_callback
id
indolem/indobert-base-uncased
finetune
mit
autotrain_compatible
endpoints_compatible
us
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damand2061/pfsa-id-med-indobert-lem
This model is a fine-tuned version of
indolem/indobert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.1033
Validation Loss: 0.2546
Validation F1: 0.8649
Validation Accuracy: 0.9290
Epoch: 4
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 19220, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
training_precision: mixed_float16
Training results
Train Loss
Validation Loss
Validation F1
Validation Accuracy
Epoch
0.3412
0.2362
0.7881
0.9230
0
0.2070
0.2131
0.8448
0.9301
1
0.1615
0.2377
0.8529
0.9254
2
0.1288
0.2406
0.8623
0.9285
3
0.1033
0.2546
0.8649
0.9290
4
Framework versions
Transformers 4.44.0
TensorFlow 2.16.1
Datasets 2.21.0
Tokenizers 0.19.1
